In the burgeoning field of artificial intelligence (AI), the unprecedented progress of large language models (LLMs) in natural language processing (NLP) offers an opportunity to revisit the entire approach of traditional metrics of machine intelligence, both in form and content. As the realm of machine cognitive evaluation has already reached Imitation, the next step is an efficient Language Acquisition and Understanding. Our paper proposes a paradigm shift from the established Turing Test towards an all-embracing framework that hinges on language acquisition, taking inspiration from the recent advancements in LLMs. The present contribution is deeply tributary of the excellent work from various disciplines, point out the need to keep interdisciplinary bridges open, and delineates a more robust and sustainable approach.

翻译：在人工智能（AI）蓬勃发展的领域中，大型语言模型（LLM）在自然语言处理（NLP）方面取得了前所未有的进展，这为我们重新审视机器智能传统度量方法的形式与内容提供了契机。当机器认知评估领域已步入“模仿”阶段时，下一步目标将是高效的语言习得与理解。本文借鉴LLM的最新进展，提出从传统的图灵测试向一个以语言习为核心的综合性框架进行范式转变。本文借鉴了多学科杰出研究成果，强调应保持跨学科桥梁的开放性，并勾勒出一种更加稳健且可持续的研究路径。